Incremental learning-based land mark recognition for mirco-UAV autonomous landing

2020 ◽  
Author(s):  
Kai Shen ◽  
Yu Zhuang ◽  
Yixiao Zhu
Keyword(s):  
Incremental Learning ◽  
Autonomous Landing

Incremental learning of word meanings: ERP evidence for stages of meaning acquisition

2010 ◽  
Author(s):  
Gwen A. Frishkoff ◽  
Kevyn Collins-Thompson ◽  
Charles A. Perfetti
Keyword(s):  
Incremental Learning ◽  
Word Meanings

Incremental learning in word production: Tracing the fate of non-selected alternative picture names.

2018 ◽  
Vol 44 (10) ◽  
pp. 1586-1602 ◽  
Author(s):  
Franziska Kurtz ◽  
Herbert Schriefers ◽  
Andreas Mädebach ◽  
Jörg D. Jescheniak
Keyword(s):  
Incremental Learning ◽  
Word Production

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

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